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- W4378464696 abstract "Grasping specific objects in complex and irregularly stacked scenes is still challenging for robotics. Because the robot is not only required to identify the object's grasping posture but also needs to reason the manipulation relationship between the objects. In this paper, we propose a manipulation relationship reasoning network with a multi-scale feature aggregation (MSFA) mechanism for robot grasping tasks. MSFA aggregates high-level semantic information and low-level spatial information in a cross-scale connection way to improve the generalization ability of the model. Furthermore, to improve the accuracy, we propose to use intersection features with rich location priors for manipulation relationship reasoning. Experiments are validated in VMRD datasets and real environments, respectively. The experimental results demonstrate that our proposed method can accurately predict the manipulation relationship between objects in the scene of multi-object stacking. Compared with previous methods, it significantly improves reasoning speed and accuracy." @default.
- W4378464696 created "2023-05-27" @default.
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- W4378464696 date "2023-05-22" @default.
- W4378464696 modified "2023-09-25" @default.
- W4378464696 title "A Single Multi-Task Deep Neural Network with a Multi-Scale Feature Aggregation Mechanism for Manipulation Relationship Reasoning in Robotic Grasping" @default.
- W4378464696 doi "https://doi.org/10.48550/arxiv.2305.13591" @default.
- W4378464696 hasPublicationYear "2023" @default.
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